Quality control - Distributions - DIPG Batch 1
Sophie Herbst
Analysis date: 2022-12-13
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_DataProcessing.RData")TODO
Setup
Load libraries and functions
Quality control
Nr. phospho sites
print( paste( nrow(pY_Set1) , "pY peptides passed the filtering procedure for Set 1. These peptides were detected from", length(unique(pY_Set1$HGNC_Symbol) ), "proteins." ))## [1] "236 pY peptides passed the filtering procedure for Set 1. These peptides were detected from 159 proteins."#print( paste( nrow(pST_Set1) , "pST peptides passed the filtering procedure for Set 1. These peptides were detected from", length(unique(pST$HGNC_Symbol) ), "proteins." ))
print( paste( nrow(pY_Set2) , "pY peptides passed the filtering procedure for Set 2. These peptides were detected from", length(unique(pY_Set2$HGNC_Symbol) ), "proteins." ))## [1] "573 pY peptides passed the filtering procedure for Set 2. These peptides were detected from 362 proteins."#print( paste( nrow(pST_Set2) , "pST peptides passed the filtering procedure for Set 2. These peptides were detected from", length(unique(pST$HGNC_Symbol) ), "proteins." ))
print( paste( nrow(pY_Set3) , "pY peptides passed the filtering procedure for Set 2. These peptides were detected from", length(unique(pY_Set3$HGNC_Symbol) ), "proteins." ))## [1] "266 pY peptides passed the filtering procedure for Set 2. These peptides were detected from 177 proteins."print( paste( nrow(pY_noNA) , "pY peptides passed the filtering procedure for the sets combined. These peptides were detected from", length(unique(pY_noNA$HGNC_Symbol) ), "proteins." ))## [1] "155 pY peptides passed the filtering procedure for the sets combined. These peptides were detected from 112 proteins."#print( paste( nrow(pST_noNA) , "pST peptides passed the filtering procedure for the sets combined. These peptides were detected from", length(unique(pST_noNA$HGNC_Symbol) ), "proteins." ))Nr. proteins whole proteome
print( paste( length(unique(prot_Set1$HGNC_Symbol) ), "proteins detected in Set 1." ))
print( paste( length(unique(prot_Set2$HGNC_Symbol) ), "proteins detected in Set 2." ))
print( paste( length(unique(prot_top3peptidemedian$HGNC_Symbol) ), "proteins detected in both Sets." ))Distributions
pY Abundances
Set 1
pY_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")## Warning: Removed 303 rows containing non-finite values (stat_density).
pY_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Abundances normalised to sup")## Warning: Removed 303 rows containing non-finite values (stat_density).
pY_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
pY_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
pY_Set1 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")
Set 2
pY_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")## Warning: Removed 645 rows containing non-finite values (stat_density).
pY_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Abundances normalised to sup")## Warning: Removed 633 rows containing non-finite values (stat_density).
pY_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
pY_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
pY_Set2 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")
Set 3
pY_Set3 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")## Warning: Removed 493 rows containing non-finite values (stat_density).
pY_Set3 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Abundances normalised to sup")## Warning: Removed 493 rows containing non-finite values (stat_density).
pY_Set3 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
pY_Set3 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
pY_Set3 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")
Combined
pY_noNA %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_"), drop = F) %>%
separate( Sample , into = c("xenograft", "treatment", "day", "replicate", "set"), sep = "_", remove = F) %>%
ggplot(aes(value, fill= treatment, group = Sample)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to normal") +
theme_bw() +
scale_fill_manual(values = PGPalette[c(1, 2, 4, 5)])
pY_noNA %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_")) %>%
separate( Sample , into = c( "xenograft", "treatment", "day", "replicate", "set"), sep = "_", remove = F) %>%
ggplot(aes(Sample, value, fill= treatment)) +
geom_boxplot() +
ggtitle("log2FC to normal") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
scale_fill_manual(values = PGPalette[c(1, 2, 4, 5)])
pST Abundances
Set 1
pST_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")
pST_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Abundances normalised to sup")
pST_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
pST_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
pST_Set1 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")Set 2
pST_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")
pST_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Abundances normalised to sup")
pST_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
pST_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
pST_Set2 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")Combined
pST_noNA %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_")) %>%
separate(Sample, into = c("treatment", "replicate"), remove = F) %>%
ggplot(aes(value, fill= treatment, group = Sample)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to normal") +
theme_bw() +
scale_fill_manual(values = PGPalette[c(1, 2, 4, 5)]) +
geom_vline(xintercept = 0)
pST_noNA %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_")) %>%
separate(Sample, into = c("treatment", "replicate"), remove = F) %>%
ggplot(aes(Sample, value, fill= treatment)) +
geom_boxplot() +
ggtitle("log2FC to bridge") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
scale_fill_manual(values = PGPalette[c(1, 2, 4, 5)])
pST_noNA %>%
select(contains("log2FC")) %>%
select(!contains("normal")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_")) %>%
separate(Sample, into = c("treatment", "replicate"), remove = F) %>%
ggplot(aes(value, fill= treatment, group = Sample)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to normal") +
theme_bw() +
scale_fill_manual(values = PGPalette[c(1, 2, 5)])Whole proteome abundances
Set 1
prot_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
geom_density(alpha=0.5) +
xlim(0,10e5) +
ggtitle("Raw abundances")
prot_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10) +
ggtitle("Abundances normalised to sup")
prot_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
prot_Set1 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
prot_Set1 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")Set 2
prot_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e5) +
ggtitle("Raw abundances")
prot_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10) +
ggtitle("Abundances normalised to sup")
prot_Set2 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Raw abundances")
prot_Set2 %>%
select(contains("TMTNorm_Abundance")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
scale_x_log10() +
ggtitle("Abundances normalised to sup")
prot_Set2 %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to bridge")Combined
prot_top3peptidemedian %>%
select(contains("log2FC")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
geom_density(alpha=0.5) +
ggtitle("log2FC to normal")
prot_top3peptidemedian %>%
select(contains("log2FC")) %>%
select(!contains("normal")) %>%
pivot_longer(names_to = "Sample", cols = everything()) %>%
mutate(Sample = str_remove(Sample, "log2FC_")) %>%
separate(Sample, into = c("treatment", "replicate"), remove = F) %>%
ggplot(aes(value, fill= treatment, group = Sample)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
ggtitle("log2FC to normal") +
theme_bw() +
scale_fill_manual(values = PGPalette[c(1, 2, 5)])
t(pST_mat_nonormal) %>%
as.data.frame( ) %>%
rownames_to_column( "peptide") %>%
pivot_longer(-peptide, names_to = "sample", values_to = "log2FC") %>%
mutate(sample = gsub( "log2FC_", "", sample)) %>%
separate(sample, into = c("treatment", "replicate"), sep = "-",remove = F) %>%
separate(peptide, into = c("HGNC_Symbol", "Annotated_Sequence"), sep = "_", remove = F ) %>%
group_by(sample, treatment, replicate) %>%
summarise("Mean of patient" = mean(log2FC)) %>%
ungroup() %>%
mutate(treatment = as.factor(treatment)) %>%
mutate(treatment = factor(treatment, levels = c("WT", "G34R", "K27M"))) %>%
ggplot(aes( treatment, `Mean of patient`, fill = treatment )) +
geom_boxplot(outlier.size = 0) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank()) +
scale_fill_manual(values = PGPalette[c(5,1,2)]) +
ggbeeswarm::geom_beeswarm() +
ggpubr::stat_compare_means(method = "t.test",
comparisons = list(c("WT", "G34R"),
c("WT", "K27M"),
c("K27M", "G34R")) ) +
ggtitle("pST median normalised log2 fold change")pY Mean-Variance Relationship
Set 1
pY_Set1 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
group_by(peptide) %>%
summarise(mean = mean(value), sd = sd (value) ) %>%
ggplot(aes(mean, sd)) +
xlim(0,10e4) +
ylim(0,10e4) +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## Warning: Removed 20 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 20 rows containing non-finite values (stat_smooth).## Warning: Removed 20 rows containing missing values (geom_point).
pY_Set1 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
mutate(log2value = log2(value) ) %>%
group_by(peptide) %>%
summarise(meanlog2 = mean(log2value), sdlog2 = sd (log2value) ) %>%
ggplot(aes(meanlog2, sdlog2)) +
#ylim(0,10e4) +
#scale_x_log10() +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## `geom_smooth()` using formula 'y ~ x'
Set 2
pY_Set2 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
group_by(peptide) %>%
summarise(mean = mean(value), sd = sd (value) ) %>%
ggplot(aes(mean, sd)) +
xlim(0,10e4) +
ylim(0,10e4) +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## Warning: Removed 44 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 44 rows containing non-finite values (stat_smooth).## Warning: Removed 44 rows containing missing values (geom_point).
pY_Set2 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
mutate(log2value = log2(value) ) %>%
group_by(peptide) %>%
summarise(meanlog2 = mean(log2value), sdlog2 = sd (log2value) ) %>%
ggplot(aes(meanlog2, sdlog2)) +
#ylim(0,10e4) +
#scale_x_log10() +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## `geom_smooth()` using formula 'y ~ x'
Set 3
pY_Set3 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
group_by(peptide) %>%
summarise(mean = mean(value), sd = sd (value) ) %>%
ggplot(aes(mean, sd)) +
xlim(0,10e4) +
ylim(0,10e4) +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## Warning: Removed 32 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 32 rows containing non-finite values (stat_smooth).## Warning: Removed 32 rows containing missing values (geom_point).
pY_Set3 %>%
mutate(peptide = paste0( HGNC_Symbol, "_", `Annotated Sequence` )) %>%
select(peptide, contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = -peptide) %>%
mutate(log2value = log2(value) ) %>%
group_by(peptide) %>%
summarise(meanlog2 = mean(log2value), sdlog2 = sd (log2value) ) %>%
ggplot(aes(meanlog2, sdlog2)) +
#ylim(0,10e4) +
#scale_x_log10() +
#geom_histogram(bins= 200) +
geom_point() +
ggtitle("pY sd vs. mean") +
ggpubr::stat_cor()+
geom_smooth(method = "lm")## `geom_smooth()` using formula 'y ~ x'
pY Abundances per peptide
Set 1
pY_Set1 %>%
select(contains("Abundance"), -contains("TMT")) %>%
pivot_longer(names_to = "Channel", cols = everything()) %>%
ggplot(aes(value, fill= Channel)) +
#geom_histogram(bins= 200) +
geom_density(alpha=0.5) +
xlim(0,10e4) +
ggtitle("Raw abundances")## Warning: Removed 303 rows containing non-finite values (stat_density).
Session Info
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forcats_0.5.2 stringr_1.4.1
## [3] dplyr_1.0.10 purrr_0.3.5
## [5] readr_2.1.3 tidyr_1.2.1
## [7] tibble_3.1.8 ggplot2_3.3.6
## [9] tidyverse_1.3.2 mdatools_0.13.0
## [11] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1
## [13] GenomeInfoDb_1.30.1 MatrixGenerics_1.6.0
## [15] matrixStats_0.62.0 DEP_1.16.0
## [17] org.Hs.eg.db_3.14.0 AnnotationDbi_1.56.2
## [19] IRanges_2.28.0 S4Vectors_0.32.4
## [21] Biobase_2.54.0 BiocGenerics_0.40.0
## [23] fgsea_1.20.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 shinydashboard_0.7.2 proto_1.0.0
## [4] gmm_1.7 tidyselect_1.2.0 RSQLite_2.2.18
## [7] htmlwidgets_1.5.4 grid_4.1.3 BiocParallel_1.28.3
## [10] norm_1.0-10.0 munsell_0.5.0 codetools_0.2-18
## [13] preprocessCore_1.56.0 chron_2.3-58 DT_0.26
## [16] withr_2.5.0 colorspace_2.0-3 highr_0.9
## [19] knitr_1.40 rstudioapi_0.14 ggsignif_0.6.4
## [22] mzID_1.32.0 labeling_0.4.2 GenomeInfoDbData_1.2.7
## [25] bit64_4.0.5 farver_2.1.1 vctrs_0.5.0
## [28] generics_0.1.3 xfun_0.34 R6_2.5.1
## [31] doParallel_1.0.17 clue_0.3-62 MsCoreUtils_1.6.2
## [34] bitops_1.0-7 cachem_1.0.6 DelayedArray_0.20.0
## [37] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.1
## [40] googlesheets4_1.0.1 gtable_0.3.1 affy_1.72.0
## [43] sandwich_3.0-2 rlang_1.0.6 mzR_2.28.0
## [46] splines_4.1.3 GlobalOptions_0.1.2 rstatix_0.7.0
## [49] gargle_1.2.1 impute_1.68.0 broom_1.0.1
## [52] BiocManager_1.30.19 yaml_2.3.6 abind_1.4-5
## [55] modelr_0.1.9 backports_1.4.1 httpuv_1.6.6
## [58] tools_4.1.3 affyio_1.64.0 ellipsis_0.3.2
## [61] gplots_3.1.3 jquerylib_0.1.4 RColorBrewer_1.1-3
## [64] STRINGdb_2.6.5 MSnbase_2.20.4 gsubfn_0.7
## [67] Rcpp_1.0.9 hash_2.2.6.2 plyr_1.8.7
## [70] zlibbioc_1.40.0 RCurl_1.98-1.9 ggpubr_0.4.0
## [73] sqldf_0.4-11 GetoptLong_1.0.5 zoo_1.8-11
## [76] haven_2.5.1 cluster_2.1.4 fs_1.5.2
## [79] magrittr_2.0.3 data.table_1.14.4 circlize_0.4.15
## [82] reprex_2.0.2 googledrive_2.0.0 pcaMethods_1.86.0
## [85] mvtnorm_1.1-3 ProtGenerics_1.26.0 hms_1.1.2
## [88] mime_0.12 evaluate_0.17 xtable_1.8-4
## [91] XML_3.99-0.12 readxl_1.4.1 gridExtra_2.3
## [94] shape_1.4.6 compiler_4.1.3 KernSmooth_2.23-20
## [97] ncdf4_1.19 crayon_1.5.2 htmltools_0.5.3
## [100] mgcv_1.8-41 later_1.3.0 tzdb_0.3.0
## [103] lubridate_1.8.0 DBI_1.1.3 dbplyr_2.2.1
## [106] ComplexHeatmap_2.10.0 MASS_7.3-58.1 tmvtnorm_1.5
## [109] Matrix_1.5-1 car_3.1-1 cli_3.4.1
## [112] vsn_3.62.0 imputeLCMD_2.1 parallel_4.1.3
## [115] igraph_1.3.5 pkgconfig_2.0.3 MALDIquant_1.21
## [118] xml2_1.3.3 foreach_1.5.2 bslib_0.4.0
## [121] XVector_0.34.0 rvest_1.0.3 digest_0.6.30
## [124] Biostrings_2.62.0 rmarkdown_2.17 cellranger_1.1.0
## [127] fastmatch_1.1-3 shiny_1.7.3 gtools_3.9.3
## [130] rjson_0.2.21 nlme_3.1-160 lifecycle_1.0.3
## [133] jsonlite_1.8.3 carData_3.0-5 limma_3.50.3
## [136] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
## [139] KEGGREST_1.34.0 fastmap_1.1.0 httr_1.4.4
## [142] plotrix_3.8-2 glue_1.6.2 png_0.1-7
## [145] iterators_1.0.14 bit_4.0.4 stringi_1.7.8
## [148] sass_0.4.2 blob_1.2.3 caTools_1.18.2
## [151] memoise_2.0.1knitr::knit_exit()